Public Sector Economic Forecasting and Econometrics Training Course

Course overview

Public-sector forecasts influence budget ceilings, tax-policy choices, debt sustainability assessments, service-demand plans and fiscal-risk statements. Yet many teams rely on spreadsheet projections that cannot explain the economic relationships behind their assumptions, distinguish cyclical from structural change, or withstand challenge from finance ministries, audit bodies and elected officials. This course equips economists, fiscal analysts and policy advisers to build forecasts that are transparent, statistically defensible and usable in formal government decision processes.

Participants develop an applied econometrics workflow for public-sector data, from defining a forecasting question and assembling time-series datasets to estimating, validating and communicating models. The course covers macro-fiscal indicators, national accounts and government finance statistics, descriptive diagnostics, regression modelling, ARIMA and dynamic forecasting, leading indicators, seasonal adjustment, panel-data methods and scenario analysis. Participants learn to test for stationarity, autocorrelation, multicollinearity and structural breaks; interpret coefficients responsibly; quantify uncertainty; and translate results into revenue, expenditure, employment, inflation and debt projections.

Instruction combines expert-led demonstrations with guided model-building in Excel, R and EViews using realistic public-finance datasets. Cases include forecasting tax receipts, modelling unemployment-benefit expenditure, estimating the effects of inflation on operating budgets and preparing alternative fiscal scenarios. By the end of the week, each participant produces a documented forecast pack containing a data dictionary, model specification, diagnostic results, baseline forecast, sensitivity scenarios, forecast-error commentary and a concise briefing suitable for senior budget or policy review.

The course is designed for professionals who already work with economic, financial or administrative data and need stronger analytical methods than descriptive reporting alone. It is particularly valuable where forecasts must be reproduced, challenged and defended across finance, policy, audit and executive stakeholders.

Course objectives

By the end of this course, participants will be able to:

• Construct a reproducible public-sector forecasting dataset using national accounts, government finance and administrative data sources
• Test time-series data for stationarity, seasonality, autocorrelation and structural breaks before model estimation
• Estimate and interpret multiple regression models for tax revenue, expenditure or service-demand forecasting
• Build ARIMA and dynamic regression forecasts with documented parameter choices and forecast horizons
• Apply panel-data methods to compare outcomes across regions, agencies or local authorities
• Produce baseline, upside and downside fiscal scenarios using transparent economic assumptions
• Evaluate forecast accuracy using residual diagnostics, out-of-sample testing and error measures
• Prepare a decision-ready forecast pack with methodology notes, charts, uncertainty ranges and policy implications

Benefits of attending

Target competencies

Who should attend

• Government Economists — who prepare macroeconomic, fiscal or sector forecasts for policy and budget decisions
• Fiscal and Budget Analysts — who need to defend revenue, expenditure and debt assumptions in budget submissions
• Central Bank and Treasury Analysts — who assess economic conditions, public finances and policy scenarios
• Public Finance Managers — who require robust projections for medium-term financial planning
• Policy Analysts — who quantify likely economic and service effects of proposed interventions
• Local Government Finance Officers — who forecast local tax bases, demand pressures and funding requirements

Requirements and prerequisites

Participants should be comfortable working with spreadsheets and interpreting tables, charts, percentages, growth rates and index numbers. Prior exposure to basic statistics, including averages, variance, correlation and confidence intervals, is expected. Participants should also understand the purpose of public budgets, revenue and expenditure categories, and common macroeconomic indicators such as GDP, inflation and unemployment. Experience with Excel is essential; prior use of R, EViews, Stata or econometric coding is helpful but not required. The course teaches the required software workflow and econometric techniques from applied foundations, but it is not suitable for attendees with no quantitative data experience.

Training methodology

The programme uses short instructor-led econometrics sessions followed by supervised model-building in Excel, R and EViews. Participants work with public-sector datasets to clean series, inspect revisions, estimate models and diagnose weaknesses in their results. Case workshops focus on tax revenue, benefit expenditure and local economic indicators, with peer challenge mirroring a budget-review meeting. Each day closes with an applied task that contributes to the final forecast pack. On day five, participants refine their own application plan for a live forecasting problem in their organisation.

Course outline

Day 1: Public-sector data and forecasting foundations

• Forecasting uses in budget preparation, fiscal strategy and policy appraisal
• National accounts, government finance statistics and administrative data sources
• Data revisions, classification changes and comparability risks
• Building a reproducible data dictionary and transformation log
• Nominal versus real values, deflators and volume measures
• Growth rates, elasticities, lags and leading indicators
• Exploratory analysis with charts, summary statistics and outlier checks

Workshop: Participants assemble and document a quarterly dataset for a public revenue or expenditure series, producing a data dictionary and initial diagnostic dashboard.

Day 2: Regression modelling for fiscal and policy variables

• Ordinary least squares regression for public-sector forecasting questions
• Selecting dependent variables, explanatory variables and lag structures
• Coefficient interpretation, elasticities and policy-relevant marginal effects
• Dummy variables for policy changes, shocks and calendar effects
• Multicollinearity, omitted-variable bias and endogeneity risks
• Residual analysis and heteroskedasticity testing
• Model specification using adjusted R-squared, AIC and BIC

Workshop: Participants estimate and interpret a tax-receipts regression model, producing a model specification note and diagnostic results table.

Day 3: Time-series econometrics and forecast accuracy

• Stationarity, unit roots and differencing
• Autocorrelation functions and partial autocorrelation functions
• ARIMA model identification, estimation and residual checks
• Seasonal adjustment and seasonal ARIMA structures
• Dynamic regression and autoregressive distributed-lag models
• Structural breaks, intervention analysis and exceptional events
• Out-of-sample validation, MAE, RMSE and forecast-bias measures

Workshop: Participants build a seasonal time-series forecast for monthly benefit expenditure and compare its accuracy against a simple trend model.

Day 4: Regional analysis and scenario-based fiscal forecasting

• Panel-data structures for regions, agencies and local authorities
• Fixed-effects and random-effects model selection
• Cross-sectional dependence and clustered standard errors
• Forecasting revenue from macroeconomic and demographic drivers
• Baseline, upside and downside scenario design
• Sensitivity analysis for inflation, employment and policy assumptions
• Debt and fiscal-risk implications of forecast revisions

Workshop: Teams develop three economic scenarios for a medium-term budget forecast, producing an assumptions register and quantified fiscal impacts.

Day 5: Forecast governance, communication and application

• Forecast governance, version control and reproducibility standards
• Documenting model limitations and expert judgement adjustments
• Prediction intervals, fan charts and uncertainty communication
• Reconciling economic forecasts with budget baselines
• Communicating technical findings to non-technical decision-makers
• Challenging forecasts in finance, audit and policy review meetings
• Designing an organisational forecasting improvement plan

Workshop: Participants complete and present a decision-ready forecast pack containing a baseline projection, scenarios, diagnostics and recommendations for their own work context.

Tools & standards covered

Microsoft Excel, RStudio, EViews, IMF Government Finance Statistics Manual 2014

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions